QP Data Analysis

rm(list = ls(all.names = TRUE)) #clear R environment

Load libraries

library(readr)
library(ggplot2)
library(GGally)
library(speakr)
library(dplyr)
library(tidyverse)
library(readr)
library(plyr)
library(stringi)
library(purrr)
library(broom)
library(scatterplot3d)
library(kableExtra)

library(tidyr)
library(readr)
library(readxl)

# get summary table for lm results
library(gtsummary)
library(finalfit) # works with plyr
library(jtools)
library(texreg)
library(kableExtra)
library(sjPlot)
library(sjmisc)
library(sjlabelled)
library(stargazer)

Loading raw data for experiment tasks

Loading .txt data anc converting .cvs to df

Loading raw data for backward

setwd("~/Desktop/RStudio_QP/experiment_rawdata")

#Load backward raw data
all_backward <- file.path("~/Desktop/RStudio_QP/experiment_rawdata/Backward") 
#Create list files
backward_file_names <- list.files(all_backward, pattern = ".txt", full.names = TRUE) 
#turns list to list object (output)
backward_data <- lapply(backward_file_names, read.table)
#change file names
names(backward_data) <- stri_sub(backward_file_names, -9, -5)

#backward_data    #use this to read df

#data frame
backward_df <- do.call("rbind", backward_data)
#converts values to string
str(backward_df)

## 'data.frame':    329 obs. of  4 variables:
##  $ V1: int  2 3 4 5 6 6 6 0 2 3 ...
##  $ V2: int  2 3 4 5 6 7 7 2 2 3 ...
##  $ V3: int  1 1 1 1 1 0 0 0 1 1 ...
##  $ V4: int  153 447 44 158 340 387 355 375 132 319 ...

backward_df <- tibble::rownames_to_column(backward_df, "row_name")
colnames(backward_df) <- c("participant_observationNum","highest_corsi","Number_of_items","backward_result","table_row")

#saving df
write.csv(backward_df,"~/Desktop/RStudio_QP/experiment_rawdata/all_backward.csv")

Loading raw data for flanker

setwd("~/Desktop/RStudio_QP/experiment_rawdata")

#Load backward raw data
all_flanker <- file.path("~/Desktop/RStudio_QP/experiment_rawdata/Flanker") 
#Create list files
flanker_file_names <- list.files(all_flanker, pattern = ".txt", full.names = TRUE) 
#turns list to list object (output)
flanker_data <- lapply(flanker_file_names, read.table)

#change file names
names(flanker_data) <- stri_sub(flanker_file_names, -9, -5)


#flanker_data       #use this only to read df

#data frame
flanker_df <- do.call("rbind", flanker_data)

str(flanker_df)

## 'data.frame':    2900 obs. of  4 variables:
##  $ V1: chr  "BBBBB" "CCXCC" "VVBVV" "BBXBB" ...
##  $ V2: int  1 1 1 0 0 0 0 1 0 1 ...
##  $ V3: int  1 1 1 1 2 1 3 2 1 1 ...
##  $ V4: int  1035 712 721 1462 1875 1242 2000 948 480 1757 ...

flanker_df <- tibble::rownames_to_column(flanker_df, "row_name")
colnames(flanker_df) <- c("participant_observationNum","stimulus", "congruent","flanker_result","response_time")

#saving df
write.csv(flanker_df,"~/Desktop/RStudio_QP/experiment_rawdata/all_flanker.csv")

Loading raw data for stroop

setwd("~/Desktop/RStudio_QP/experiment_rawdata")

#Load backward raw data
all_stroop <- file.path("~/Desktop/RStudio_QP/experiment_rawdata/Stroop") 
#Create list files
stroop_file_names <- list.files(all_stroop, pattern = ".txt", full.names = TRUE) 
#turns list to list object (output)
stroop_data <- lapply(stroop_file_names, read.table)

#change file names
names(stroop_data) <- stri_sub(stroop_file_names, -9, -5)

#stroop_data        #use this only to read df

#data frame
stroop_df <- do.call("rbind", stroop_data)

str(stroop_df)

## 'data.frame':    2320 obs. of  8 variables:
##  $ V1: chr  "training" "training" "training" "training" ...
##  $ V2: chr  "blue" "green" "yellow" "blue" ...
##  $ V3: chr  "green" "yellow" "green" "red" ...
##  $ V4: int  0 0 0 0 1 0 1 0 0 0 ...
##  $ V5: int  14 9 2 16 8 4 15 6 6 14 ...
##  $ V6: int  3 4 2 1 1 1 3 2 2 2 ...
##  $ V7: int  2 1 1 1 1 1 1 1 1 1 ...
##  $ V8: int  1353 1271 1216 1113 1333 1458 1284 1222 1244 1104 ...

stroop_df <- tibble::rownames_to_column(stroop_df, "row_name")
colnames(stroop_df) <- c("participant_observationNum","stimulus", "word_name","word_color","stroop_match", "row_number","key_pressed", "stroop_result", "response_time")

#saving df
write.csv(stroop_df,"~/Desktop/RStudio_QP/experiment_rawdata/all_stroop.csv")

#Analysis of all_data ## Tidy all_data all_data df includes all 58 participants, two groups. Only total correct responses are included for each experimental task (flanker, stroop, backward).

#Load working data
all_data <- read.csv("~/Desktop/RStudio_QP/experiment_data/all_data.csv", header = TRUE) 

#summary
summary(all_data)

##  participant             age              IQ         backward_correct
##  Length:58          Min.   :22.00   Min.   : 60.00   Min.   :0.000   
##  Class :character   1st Qu.:32.00   1st Qu.: 96.25   1st Qu.:1.000   
##  Mode  :character   Median :38.00   Median :107.00   Median :4.000   
##                     Mean   :39.19   Mean   :108.09   Mean   :3.138   
##                     3rd Qu.:47.00   3rd Qu.:122.25   3rd Qu.:5.000   
##                     Max.   :59.00   Max.   :146.00   Max.   :6.000   
##  flanker_correct stroop_correct 
##  Min.   : 0.00   Min.   : 0.00  
##  1st Qu.:40.50   1st Qu.:38.00  
##  Median :45.00   Median :39.00  
##  Mean   :41.91   Mean   :36.83  
##  3rd Qu.:47.00   3rd Qu.:40.00  
##  Max.   :49.00   Max.   :40.00

#adding "bilingual"column to data set (all_data)
all_data$bilingual = c(rep(0,36),rep(1,22)) # 0=nonLanguage, 1=language


#separate groups: nonLanguage teachers and language teachers
nonLanguage <- all_data[1:36, ]
language <- all_data[37:58, ]

nonLanguage$language = "NonLanguage"
language$language = "Language"

#adding column to indicate nonLanguage or language for each participant
all_data = bind_rows(nonLanguage,language)

##Analysis of each experiment + group+age+IQ

stroop_data = lm(stroop_correct ~ bilingual+age+IQ, data=all_data)
summary(stroop_data)

## 
## Call:
## lm(formula = stroop_correct ~ bilingual + age + IQ, data = all_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.427  -0.698   1.080   2.830   8.975 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23.81077    6.56531   3.627 0.000637 ***
## bilingual   -0.07753    1.84059  -0.042 0.966556    
## age         -0.10015    0.08968  -1.117 0.269051    
## IQ           0.15701    0.04838   3.245 0.002017 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.644 on 54 degrees of freedom
## Multiple R-squared:  0.1905, Adjusted R-squared:  0.1455 
## F-statistic: 4.235 on 3 and 54 DF,  p-value: 0.009247

flanker_data = lm(flanker_correct ~ bilingual+age+IQ, data=all_data)
summary(flanker_data)

## 
## Call:
## lm(formula = flanker_correct ~ bilingual + age + IQ, data = all_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.778  -1.168   1.397   4.657  10.463 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 32.33555    7.96362   4.060 0.000159 ***
## bilingual   -0.32869    2.23261  -0.147 0.883504    
## age         -0.14152    0.10878  -1.301 0.198803    
## IQ           0.14108    0.05869   2.404 0.019679 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.059 on 54 degrees of freedom
## Multiple R-squared:  0.1338, Adjusted R-squared:  0.08569 
## F-statistic: 2.781 on 3 and 54 DF,  p-value: 0.04972

backward_data = lm(backward_correct ~ bilingual+age+IQ, data=all_data)
summary(backward_data)

## 
## Call:
## lm(formula = backward_correct ~ bilingual + age + IQ, data = all_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6784 -1.4709  0.4089  1.3641  2.6331 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.94033    1.83101  -0.514  0.60966   
## bilingual    0.56647    0.51333   1.104  0.27469   
## age         -0.02540    0.02501  -1.016  0.31429   
## IQ           0.04495    0.01349   3.332  0.00156 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.853 on 54 degrees of freedom
## Multiple R-squared:  0.1901, Adjusted R-squared:  0.1451 
## F-statistic: 4.225 on 3 and 54 DF,  p-value: 0.009362

Analsyis by groups

Data Analysis for nonLanguage Group The nonLanguage group consist of the teachers who only use one language in their professional work setting.

Linear regression model for nonLanguage BACKWARD task

data(nonLanguage)

## Warning in data(nonLanguage): data set 'nonLanguage' not found

head(nonLanguage)

##   participant age  IQ backward_correct flanker_correct stroop_correct bilingual
## 1       adnm2  36 135                5              45             39         0
## 2       bjfc1  40 108                4              44             38         0
## 3       buzt0  30 146                5              49             39         0
## 4       bwat2  34 128                1              46             39         0
## 5       cbky9  49 135                4              48             40         0
## 6       cfug6  34  63                0              27              2         0
##      language
## 1 NonLanguage
## 2 NonLanguage
## 3 NonLanguage
## 4 NonLanguage
## 5 NonLanguage
## 6 NonLanguage

##plot
plot(backward_correct ~ age + IQ, data = nonLanguage, pch=16)

#linear model
backward_model_nonLanguage <- lm(backward_correct ~ age + IQ, data = nonLanguage)
#abline(backward_model_nonLanguage)

my_results <- tidy(backward_model_nonLanguage)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept) -0.194      2.35     -0.0828  0.934 
## 2 age         -0.00622    0.0366   -0.170   0.866 
## 3 IQ           0.0316     0.0154    2.05    0.0482

backward_model_nonLanguage

## 
## Call:
## lm(formula = backward_correct ~ age + IQ, data = nonLanguage)
## 
## Coefficients:
## (Intercept)          age           IQ  
##   -0.194378    -0.006223     0.031591

summary(backward_model_nonLanguage)

## 
## Call:
## lm(formula = backward_correct ~ age + IQ, data = nonLanguage)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3658 -2.0974  0.6713  1.3846  2.6145 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.194378   2.346418  -0.083   0.9345  
## age         -0.006223   0.036562  -0.170   0.8659  
## IQ           0.031591   0.015400   2.051   0.0482 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.934 on 33 degrees of freedom
## Multiple R-squared:  0.1174, Adjusted R-squared:  0.06393 
## F-statistic: 2.195 on 2 and 33 DF,  p-value: 0.1273

par(mfrow = c(2,2)) 
plot(backward_model_nonLanguage) 

AIC(backward_model_nonLanguage)

## [1] 154.5309

BIC(backward_model_nonLanguage)

## [1] 160.865

###
test2 = lm(backward_correct ~ bilingual+age+IQ, data=all_data)
summary(test2)

## 
## Call:
## lm(formula = backward_correct ~ bilingual + age + IQ, data = all_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6784 -1.4709  0.4089  1.3641  2.6331 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.94033    1.83101  -0.514  0.60966   
## bilingual    0.56647    0.51333   1.104  0.27469   
## age         -0.02540    0.02501  -1.016  0.31429   
## IQ           0.04495    0.01349   3.332  0.00156 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.853 on 54 degrees of freedom
## Multiple R-squared:  0.1901, Adjusted R-squared:  0.1451 
## F-statistic: 4.225 on 3 and 54 DF,  p-value: 0.009362

Linear regression model for nonLanguage FLANKER task

data(nonLanguage)

## Warning in data(nonLanguage): data set 'nonLanguage' not found

head(nonLanguage)

##   participant age  IQ backward_correct flanker_correct stroop_correct bilingual
## 1       adnm2  36 135                5              45             39         0
## 2       bjfc1  40 108                4              44             38         0
## 3       buzt0  30 146                5              49             39         0
## 4       bwat2  34 128                1              46             39         0
## 5       cbky9  49 135                4              48             40         0
## 6       cfug6  34  63                0              27              2         0
##      language
## 1 NonLanguage
## 2 NonLanguage
## 3 NonLanguage
## 4 NonLanguage
## 5 NonLanguage
## 6 NonLanguage

##plot
plot(flanker_correct ~ age + IQ, data = nonLanguage, pch=16)

#linear model
flanker_model_nonLanguage <- lm(flanker_correct ~ age + IQ, data = nonLanguage)
#abline(flanker_model_nonLanguage)

my_results <- tidy(flanker_model_nonLanguage)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic  p.value
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)  28.1       6.91       4.07  0.000278
## 2 age           0.0766    0.108      0.711 0.482   
## 3 IQ            0.104     0.0454     2.30  0.0282

flanker_model_nonLanguage

## 
## Call:
## lm(formula = flanker_correct ~ age + IQ, data = nonLanguage)
## 
## Coefficients:
## (Intercept)          age           IQ  
##    28.11861      0.07663      0.10418

summary(flanker_model_nonLanguage)

## 
## Call:
## lm(formula = flanker_correct ~ age + IQ, data = nonLanguage)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.9680  -0.4283   1.4999   3.2503   8.2520 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28.11861    6.91314   4.067 0.000278 ***
## age          0.07663    0.10772   0.711 0.481859    
## IQ           0.10418    0.04537   2.296 0.028156 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.699 on 33 degrees of freedom
## Multiple R-squared:  0.1419, Adjusted R-squared:  0.08992 
## F-statistic: 2.729 on 2 and 33 DF,  p-value: 0.08002

par(mfrow = c(2,2)) 
plot(flanker_model_nonLanguage) 

AIC(flanker_model_nonLanguage)

## [1] 232.3294

BIC(flanker_model_nonLanguage)

## [1] 238.6635

#comparing language and nonLanguage groups for flanker_correct
test = lm(flanker_correct ~ bilingual+age+IQ, data=all_data)
summary(test)

## 
## Call:
## lm(formula = flanker_correct ~ bilingual + age + IQ, data = all_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.778  -1.168   1.397   4.657  10.463 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 32.33555    7.96362   4.060 0.000159 ***
## bilingual   -0.32869    2.23261  -0.147 0.883504    
## age         -0.14152    0.10878  -1.301 0.198803    
## IQ           0.14108    0.05869   2.404 0.019679 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.059 on 54 degrees of freedom
## Multiple R-squared:  0.1338, Adjusted R-squared:  0.08569 
## F-statistic: 2.781 on 3 and 54 DF,  p-value: 0.04972

Linear regression model for nonLanguage STROOP task

data(nonLanguage)

## Warning in data(nonLanguage): data set 'nonLanguage' not found

head(nonLanguage)

##   participant age  IQ backward_correct flanker_correct stroop_correct bilingual
## 1       adnm2  36 135                5              45             39         0
## 2       bjfc1  40 108                4              44             38         0
## 3       buzt0  30 146                5              49             39         0
## 4       bwat2  34 128                1              46             39         0
## 5       cbky9  49 135                4              48             40         0
## 6       cfug6  34  63                0              27              2         0
##      language
## 1 NonLanguage
## 2 NonLanguage
## 3 NonLanguage
## 4 NonLanguage
## 5 NonLanguage
## 6 NonLanguage

##plot
plot(stroop_correct ~ age + IQ, data = nonLanguage, pch=16)

#linear model
stroop_model_nonLanguage <- lm(stroop_correct ~ age + IQ, data = nonLanguage)
abline(stroop_model_nonLanguage)

## Warning in abline(stroop_model_nonLanguage): only using the first two of 3
## regression coefficients

my_results <- tidy(stroop_model_nonLanguage)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)  19.4       6.94       2.79  0.00863
## 2 age           0.0714    0.108      0.660 0.514  
## 3 IQ            0.138     0.0456     3.03  0.00474

stroop_model_nonLanguage

## 
## Call:
## lm(formula = stroop_correct ~ age + IQ, data = nonLanguage)
## 
## Coefficients:
## (Intercept)          age           IQ  
##    19.38713      0.07142      0.13802

summary(stroop_model_nonLanguage)

## 
## Call:
## lm(formula = stroop_correct ~ age + IQ, data = nonLanguage)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.5108  -1.5472   0.5263   2.1878   7.3319 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 19.38713    6.94219   2.793  0.00863 **
## age          0.07142    0.10817   0.660  0.51370   
## IQ           0.13802    0.04556   3.029  0.00474 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.723 on 33 degrees of freedom
## Multiple R-squared:  0.2191, Adjusted R-squared:  0.1718 
## F-statistic:  4.63 on 2 and 33 DF,  p-value: 0.01689

par(mfrow = c(2,2)) 
plot(stroop_model_nonLanguage) 

AIC(stroop_model_nonLanguage)

## [1] 232.6312

BIC(stroop_model_nonLanguage)

## [1] 238.9653

test3 = lm(stroop_correct ~ bilingual+age+IQ, data=all_data)
summary(test3)

## 
## Call:
## lm(formula = stroop_correct ~ bilingual + age + IQ, data = all_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.427  -0.698   1.080   2.830   8.975 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23.81077    6.56531   3.627 0.000637 ***
## bilingual   -0.07753    1.84059  -0.042 0.966556    
## age         -0.10015    0.08968  -1.117 0.269051    
## IQ           0.15701    0.04838   3.245 0.002017 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.644 on 54 degrees of freedom
## Multiple R-squared:  0.1905, Adjusted R-squared:  0.1455 
## F-statistic: 4.235 on 3 and 54 DF,  p-value: 0.009247

Data Analysis for Language Group

The language group consist of the teachers who use more than one language in their professional work setting.

Linear regression model for language BACKWARD

data(language)

## Warning in data(language): data set 'language' not found

head(language)

##    participant age  IQ backward_correct flanker_correct stroop_correct
## 37    bi_ajli0  35  94                4              45             38
## 38    bi_dckv1  30 105                4              49             38
## 39    bi_dvyf7  58 105                5              37             39
## 40    bi_evur7  42  93                0              40             34
## 41    bi_giah0  23 102                5              44             39
## 42    bi_igsy3  50 120                6              49             39
##    bilingual language
## 37         1 Language
## 38         1 Language
## 39         1 Language
## 40         1 Language
## 41         1 Language
## 42         1 Language

#plot Language
plot(backward_correct ~ age + IQ, data = language, pch=16)

#linear model
backward_model_language <- lm(backward_correct ~ age + IQ, data = language)
abline(backward_model_language)

## Warning in abline(backward_model_language): only using the first two of 3
## regression coefficients

my_results <- tidy(backward_model_language)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic   p.value
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)  -7.69      2.80       -2.75 0.0128   
## 2 age          -0.0609    0.0269     -2.27 0.0352   
## 3 IQ            0.129     0.0258      5.00 0.0000795

backward_model_language

## 
## Call:
## lm(formula = backward_correct ~ age + IQ, data = language)
## 
## Coefficients:
## (Intercept)          age           IQ  
##    -7.69311     -0.06092      0.12908

summary(backward_model_language)

## 
## Call:
## lm(formula = backward_correct ~ age + IQ, data = language)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.32845 -0.81186 -0.07596  0.90055  2.67241 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -7.69311    2.79829  -2.749   0.0128 *  
## age         -0.06092    0.02687  -2.267   0.0352 *  
## IQ           0.12908    0.02582   5.000 7.95e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.375 on 19 degrees of freedom
## Multiple R-squared:  0.5937, Adjusted R-squared:  0.5509 
## F-statistic: 13.88 on 2 and 19 DF,  p-value: 0.0001923

par(mfrow = c(2,2)) 
plot(backward_model_language) 

AIC(backward_model_language)

## [1] 81.20737

BIC(backward_model_language)

## [1] 85.57154

Linear regression model for language FLANKER

data(language)

## Warning in data(language): data set 'language' not found

head(language)

##    participant age  IQ backward_correct flanker_correct stroop_correct
## 37    bi_ajli0  35  94                4              45             38
## 38    bi_dckv1  30 105                4              49             38
## 39    bi_dvyf7  58 105                5              37             39
## 40    bi_evur7  42  93                0              40             34
## 41    bi_giah0  23 102                5              44             39
## 42    bi_igsy3  50 120                6              49             39
##    bilingual language
## 37         1 Language
## 38         1 Language
## 39         1 Language
## 40         1 Language
## 41         1 Language
## 42         1 Language

#plot Language
plot(flanker_correct ~ age + IQ, data = language, pch=16)

#linear model
flanker_model_language <- lm(flanker_correct ~ age + IQ, data = language)
abline(flanker_model_language)

## Warning in abline(flanker_model_language): only using the first two of 3
## regression coefficients

my_results <- tidy(flanker_model_language)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)   12.1      20.1       0.605  0.552 
## 2 age           -0.425     0.193    -2.21   0.0399
## 3 IQ             0.443     0.185     2.39   0.0271

flanker_model_language

## 
## Call:
## lm(formula = flanker_correct ~ age + IQ, data = language)
## 
## Coefficients:
## (Intercept)          age           IQ  
##     12.1411      -0.4251       0.4432

summary(flanker_model_language)

## 
## Call:
## lm(formula = flanker_correct ~ age + IQ, data = language)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.278  -1.917   2.854   4.847  11.242 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  12.1411    20.0687   0.605   0.5523  
## age          -0.4251     0.1927  -2.207   0.0399 *
## IQ            0.4432     0.1851   2.394   0.0271 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.858 on 19 degrees of freedom
## Multiple R-squared:  0.3319, Adjusted R-squared:  0.2616 
## F-statistic: 4.719 on 2 and 19 DF,  p-value: 0.02168

par(mfrow = c(2,2)) 
plot(flanker_model_language) 

AIC(flanker_model_language)

## [1] 167.8941

BIC(flanker_model_language)

## [1] 172.2582

Linear regression model for language STROOP

data(language)

## Warning in data(language): data set 'language' not found

head(language)

##    participant age  IQ backward_correct flanker_correct stroop_correct
## 37    bi_ajli0  35  94                4              45             38
## 38    bi_dckv1  30 105                4              49             38
## 39    bi_dvyf7  58 105                5              37             39
## 40    bi_evur7  42  93                0              40             34
## 41    bi_giah0  23 102                5              44             39
## 42    bi_igsy3  50 120                6              49             39
##    bilingual language
## 37         1 Language
## 38         1 Language
## 39         1 Language
## 40         1 Language
## 41         1 Language
## 42         1 Language

#plot Language
par(mfrow = c(2,2))
plot(stroop_correct ~ age + IQ, data = language, pch=16)

#linear model
stroop_model_language <- lm(stroop_correct ~ age + IQ, data = language)

par(mfrow = c(2,2))

plot(stroop_model_language)

my_results <- tidy(stroop_model_language)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)   13.6      14.9       0.914  0.372 
## 2 age           -0.313     0.143    -2.18   0.0417
## 3 IQ             0.338     0.138     2.45   0.0240

stroop_model_language

## 
## Call:
## lm(formula = stroop_correct ~ age + IQ, data = language)
## 
## Coefficients:
## (Intercept)          age           IQ  
##     13.6334      -0.3127       0.3376

summary(stroop_model_language)

## 
## Call:
## lm(formula = stroop_correct ~ age + IQ, data = language)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -26.5774  -1.6728   0.3679   2.9557  11.9590 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  13.6334    14.9160   0.914   0.3722  
## age          -0.3127     0.1432  -2.184   0.0417 *
## IQ            0.3376     0.1376   2.453   0.0240 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.327 on 19 degrees of freedom
## Multiple R-squared:  0.3359, Adjusted R-squared:  0.266 
## F-statistic: 4.805 on 2 and 19 DF,  p-value: 0.02048

par(mfrow = c(2,2)) 
plot(stroop_model_language) 

AIC(stroop_model_language)

## [1] 154.8383

BIC(stroop_model_language)

## [1] 159.2025

#ADDITIONAL ANALYSIS

Histogram to observe results

attach(all_data) #using objects in df all_data

## The following object is masked _by_ .GlobalEnv:
## 
##     language

hist(backward_correct) #this could be considered "normal"

hist(flanker_correct) #not normal

hist(stroop_correct) #not normal

summary(all_data)

##  participant             age              IQ         backward_correct
##  Length:58          Min.   :22.00   Min.   : 60.00   Min.   :0.000   
##  Class :character   1st Qu.:32.00   1st Qu.: 96.25   1st Qu.:1.000   
##  Mode  :character   Median :38.00   Median :107.00   Median :4.000   
##                     Mean   :39.19   Mean   :108.09   Mean   :3.138   
##                     3rd Qu.:47.00   3rd Qu.:122.25   3rd Qu.:5.000   
##                     Max.   :59.00   Max.   :146.00   Max.   :6.000   
##  flanker_correct stroop_correct    bilingual        language        
##  Min.   : 0.00   Min.   : 0.00   Min.   :0.0000   Length:58         
##  1st Qu.:40.50   1st Qu.:38.00   1st Qu.:0.0000   Class :character  
##  Median :45.00   Median :39.00   Median :0.0000   Mode  :character  
##  Mean   :41.91   Mean   :36.83   Mean   :0.3793                     
##  3rd Qu.:47.00   3rd Qu.:40.00   3rd Qu.:1.0000                     
##  Max.   :49.00   Max.   :40.00   Max.   :1.0000

#————————————————————————-#

Removing 1 data (45) which is considered an outlier.

#created new data with deleted row 45
new_all_data <- all_data[-c(45),]

#created new language group (-45)
new_language <- new_all_data[37:58, ]

summary(new_all_data)

##  participant             age              IQ        backward_correct
##  Length:57          Min.   :22.00   Min.   : 60.0   Min.   :0.000   
##  Class :character   1st Qu.:32.00   1st Qu.: 97.0   1st Qu.:1.000   
##  Mode  :character   Median :38.00   Median :107.0   Median :4.000   
##                     Mean   :38.84   Mean   :108.4   Mean   :3.193   
##                     3rd Qu.:47.00   3rd Qu.:123.0   3rd Qu.:5.000   
##                     Max.   :58.00   Max.   :146.0   Max.   :6.000   
##  flanker_correct stroop_correct    bilingual        language        
##  Min.   :26.00   Min.   : 2.00   Min.   :0.0000   Length:57         
##  1st Qu.:42.00   1st Qu.:38.00   1st Qu.:0.0000   Class :character  
##  Median :45.00   Median :39.00   Median :0.0000   Mode  :character  
##  Mean   :42.65   Mean   :37.47   Mean   :0.3684                     
##  3rd Qu.:47.00   3rd Qu.:40.00   3rd Qu.:1.0000                     
##  Max.   :49.00   Max.   :40.00   Max.   :1.0000

Linear regression for Stroop

#Data analysis of 3 tasks and groups with "new_all_data" df
stroop_data = lm(stroop_correct ~ bilingual+age+IQ, data=new_all_data)
summary(stroop_data)

## 
## Call:
## lm(formula = stroop_correct ~ bilingual + age + IQ, data = new_all_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.7243  -1.5098   0.4402   2.1226   7.5599 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 21.46556    4.72117   4.547  3.2e-05 ***
## bilingual    1.09761    1.33047   0.825 0.413081    
## age          0.01630    0.06634   0.246 0.806907    
## IQ           0.13817    0.03481   3.970 0.000218 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.766 on 53 degrees of freedom
## Multiple R-squared:  0.2303, Adjusted R-squared:  0.1868 
## F-statistic: 5.287 on 3 and 53 DF,  p-value: 0.002903

#table for summary results
tbl_regression(stroop_data, exponentiate = FALSE) #created table

Characteristic	Beta	95% CI1	p-value
bilingual	1.1	-1.6, 3.8	0.4
age	0.02	-0.12, 0.15	0.8
IQ	0.14	0.07, 0.21	<0.001
1 CI = Confidence Interval

summ(stroop_data, scale = FALSE) #another table

Observations	57
Dependent variable	stroop_correct
Type	OLS linear regression

F(3,53)	5.29
R²	0.23
Adj. R²	0.19

	Est.	S.E.	t val.	p
(Intercept)	21.47	4.72	4.55	0.00
bilingual	1.10	1.33	0.82	0.41
age	0.02	0.07	0.25	0.81
IQ	0.14	0.03	3.97	0.00
Standard errors: OLS

broom::tidy(stroop_data) #table for summary

## # A tibble: 4 × 5
##   term        estimate std.error statistic   p.value
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)  21.5       4.72       4.55  0.0000320
## 2 bilingual     1.10      1.33       0.825 0.413    
## 3 age           0.0163    0.0663     0.246 0.807    
## 4 IQ            0.138     0.0348     3.97  0.000218

#Table for Stroop lm results
tab_model(stroop_data,
          dv.labels = c("Stroop Task"),
          string.pred = "Coefficient",
          string.p ="P-Value")#printed table for summary

	Stroop Task
Coefficient	Estimates	CI	P-Value
(Intercept)	21.47	12.00 – 30.94	<0.001
bilingual	1.10	-1.57 – 3.77	0.413
age	0.02	-0.12 – 0.15	0.807
IQ	0.14	0.07 – 0.21	<0.001
Observations	57
R2 / R2 adjusted	0.230 / 0.187

#plot IQ
ggplot(new_all_data, aes(x =IQ, y = stroop_correct, color = language)) +
  geom_point() +
   scale_fill_discrete(labels = c('Languages','One Language')) +
  guides(colour = guide_legend(title = "Teachers")) +
labs(title = "Stroop Task Performance", y= "Correct Trials", x= "Intelligence Quotient") +
  theme(plot.title=element_text(hjust=0.5))

#plot age
ggplot(new_all_data, aes(x =age, y = stroop_correct, color = language)) +
  geom_point() +
   scale_fill_discrete(labels = c('Languages','One Language')) +
  guides(colour = guide_legend(title = "Teachers")) +
labs(title = "Stroop Task Performance", y= "Correct Trials", x= "Age") +
  theme(plot.title=element_text(hjust=0.5))

##Assumptions of Linear Regression *Keys: 1, 2, 3, 4, 5. Ex. plot(stroop_data, 5)
par(mfrow = c(2,2))
plot(stroop_data)

flanker_data = lm(flanker_correct ~ bilingual+age+IQ, data=new_all_data)
summary(flanker_data)

## 
## Call:
## lm(formula = flanker_correct ~ bilingual + age + IQ, data = new_all_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16.9626  -0.5461   1.5371   3.9393   9.1539 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29.675659   6.069105   4.890 9.78e-06 ***
## bilingual    1.004132   1.710328   0.587  0.55963    
## age         -0.009448   0.085276  -0.111  0.91220    
## IQ           0.119708   0.044746   2.675  0.00991 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.127 on 53 degrees of freedom
## Multiple R-squared:   0.12,  Adjusted R-squared:  0.07024 
## F-statistic:  2.41 on 3 and 53 DF,  p-value: 0.07715

tbl_regression(flanker_data, exponentiate = FALSE,) #created table

Characteristic	Beta	95% CI1	p-value
bilingual	1.0	-2.4, 4.4	0.6
age	-0.01	-0.18, 0.16	>0.9
IQ	0.12	0.03, 0.21	0.010
1 CI = Confidence Interval

summ(flanker_data, scale = FALSE) #another table

Observations	57
Dependent variable	flanker_correct
Type	OLS linear regression

F(3,53)	2.41
R²	0.12
Adj. R²	0.07

	Est.	S.E.	t val.	p
(Intercept)	29.68	6.07	4.89	0.00
bilingual	1.00	1.71	0.59	0.56
age	-0.01	0.09	-0.11	0.91
IQ	0.12	0.04	2.68	0.01
Standard errors: OLS

broom::tidy(flanker_data) #table for summary

## # A tibble: 4 × 5
##   term        estimate std.error statistic    p.value
##   <chr>          <dbl>     <dbl>     <dbl>      <dbl>
## 1 (Intercept) 29.7        6.07       4.89  0.00000978
## 2 bilingual    1.00       1.71       0.587 0.560     
## 3 age         -0.00945    0.0853    -0.111 0.912     
## 4 IQ           0.120      0.0447     2.68  0.00991

#Table for Flanker lm results
tab_model(flanker_data,
          dv.labels = c("Flanker Task"),
          string.pred = "Coeffcient",
          string.ci = "Conf. Int",
          string.p ="P-Value")#printed table for summary

	Flanker Task
Coeffcient	Estimates	Conf. Int	P-Value
(Intercept)	29.68	17.50 – 41.85	<0.001
bilingual	1.00	-2.43 – 4.43	0.560
age	-0.01	-0.18 – 0.16	0.912
IQ	0.12	0.03 – 0.21	0.010
Observations	57
R2 / R2 adjusted	0.120 / 0.070

#plot IQ
ggplot(new_all_data, aes(x =IQ, y = flanker_correct, color = language)) +
  geom_point() +
   scale_fill_discrete(labels=c('Languages','One Language')) +
  guides(colour = guide_legend(title = "Teachers")) +
labs(title = "Flanker Task Performance", y= "Correct Trials", x= "Intelligence Quotient") +
  theme(plot.title=element_text(hjust=0.5))

#plot AGE
ggplot(new_all_data, aes(x = age, y = flanker_correct, color = language)) +
  geom_point() +
   scale_fill_discrete(labels = c('Languages','One Language')) +
  guides(colour = guide_legend(title = "Teachers")) +
labs(title = "Flanker Task Performance", y= "Correct Trials", x= "Age") +
  theme(plot.title=element_text(hjust=0.5))

par(mfrow = c(2,2))
plot(flanker_data)

backward_data = lm(backward_correct ~ bilingual+age+IQ, data=new_all_data)
summary(backward_data)

## 
## Call:
## lm(formula = backward_correct ~ bilingual + age + IQ, data = new_all_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5770 -1.1122  0.3912  1.3215  2.7331 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -1.10726    1.82251  -0.608  0.54608   
## bilingual    0.65011    0.51360   1.266  0.21112   
## age         -0.01712    0.02561  -0.668  0.50677   
## IQ           0.04361    0.01344   3.246  0.00203 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.84 on 53 degrees of freedom
## Multiple R-squared:  0.1803, Adjusted R-squared:  0.1339 
## F-statistic: 3.886 on 3 and 53 DF,  p-value: 0.01387

tbl_regression(backward_data, exponentiate = FALSE) #created table

Characteristic	Beta	95% CI1	p-value
bilingual	0.65	-0.38, 1.7	0.2
age	-0.02	-0.07, 0.03	0.5
IQ	0.04	0.02, 0.07	0.002
1 CI = Confidence Interval

summ(backward_data, scale = FALSE) #another table

Observations	57
Dependent variable	backward_correct
Type	OLS linear regression

F(3,53)	3.89
R²	0.18
Adj. R²	0.13

	Est.	S.E.	t val.	p
(Intercept)	-1.11	1.82	-0.61	0.55
bilingual	0.65	0.51	1.27	0.21
age	-0.02	0.03	-0.67	0.51
IQ	0.04	0.01	3.25	0.00
Standard errors: OLS

#Table for backward corsi lm results
tab_model(backward_data,
          dv.labels = c("Backward Corsi Task"),
          string.pred = "Coeffcient",
          string.p ="P-Value")#printed table for summary

	Backward Corsi Task
Coeffcient	Estimates	CI	P-Value
(Intercept)	-1.11	-4.76 – 2.55	0.546
bilingual	0.65	-0.38 – 1.68	0.211
age	-0.02	-0.07 – 0.03	0.507
IQ	0.04	0.02 – 0.07	0.002
Observations	57
R2 / R2 adjusted	0.180 / 0.134

ggplot(new_all_data, aes(x =IQ, y = backward_correct, color = language)) +
  geom_point() +
   scale_fill_discrete(labels = c('Languages','One Language')) +
  guides(colour = guide_legend(title = "Teachers")) +
labs(title = "Backward Corsi Task Performance", y= "Correct Trials", x= "Intelligence Quotient") +
  theme(plot.title=element_text(hjust=0.5))

#plot AGE
ggplot(new_all_data, aes(x =age, y = backward_correct, color = language)) +
  geom_point() +
   scale_fill_discrete(labels = c('Languages','One Language')) +
  guides(colour = guide_legend(title = "Teachers")) +
labs(title = "Backward Corsi Task Performance", y= "Correct Trials", x= "Age") +
  theme(plot.title=element_text(hjust=0.5))

par(mfrow = c(2,2))
plot(backward_data)

## Print all 3 regression results

tab_model(flanker_data, backward_data, stroop_data,
          dv.labels = c("Flanker Task", "Backward Corsi Task", "Stroop Task"),
string.p ="P-Value")#printed table for summary

	Flanker Task			Backward Corsi Task			Stroop Task
Predictors	Estimates	CI	P-Value	Estimates	CI	P-Value	Estimates	CI	P-Value
(Intercept)	29.68	17.50 – 41.85	<0.001	-1.11	-4.76 – 2.55	0.546	21.47	12.00 – 30.94	<0.001
bilingual	1.00	-2.43 – 4.43	0.560	0.65	-0.38 – 1.68	0.211	1.10	-1.57 – 3.77	0.413
age	-0.01	-0.18 – 0.16	0.912	-0.02	-0.07 – 0.03	0.507	0.02	-0.12 – 0.15	0.807
IQ	0.12	0.03 – 0.21	0.010	0.04	0.02 – 0.07	0.002	0.14	0.07 – 0.21	<0.001
Observations	57			57			57
R2 / R2 adjusted	0.120 / 0.070			0.180 / 0.134			0.230 / 0.187

Background Questionnaire

background_survey <- read.csv("~/Desktop/RStudio_QP/experiment_data/background_survey.csv", header = TRUE)


#background_survey <- background_survey %>% select(-c(X,Please.enter.your.Prolific.ID...Participant.ID..or.full.name,X.1))

#background_survey %>%
  #kbl(caption = "Background data") %>%
 # kable_classic(full_width = F, html_font = "Cambria") 

#str(background_survey)

str(background_survey)

## 'data.frame':    781 obs. of  11 variables:
##  $ X                                                           : chr  "nvko1" "yrtb9" "ejpg7" "vvpq4" ...
##  $ Please.enter.your.Prolific.ID...Participant.ID..or.full.name: chr  "5ffc97615991d411f8ceff96" "5f22bdb732432502a2615f3f" "5fd4125ed9973916d62d9958" "5e483052a742a3000f7c15e8" ...
##  $ X.1                                                         : logi  NA NA NA NA NA NA ...
##  $ Bilingual                                                   : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Gender                                                      : chr  "M" "F" "M" "F" ...
##  $ Residence                                                   : chr  "USA" "USA" "USA" "USA" ...
##  $ Age                                                         : chr  "22" "30" "32" "32" ...
##  $ Ethnicity                                                   : chr  "White or Caucasian" "White or Caucasian" "Asian or Pacific Islander" "White or Caucasian" ...
##  $ Teaching_Level                                              : chr  "9-12 grade" "6-8 grades" "9-12 grade" "9-12 grade" ...
##  $ Experience                                                  : chr  "1-3 years" "7-10 years" "4-6 years" "7-10 years" ...
##  $ Languages                                                   : chr  NA NA NA NA ...

background_survey$Age <- as.numeric(background_survey$Age)

## Warning: NAs introduced by coercion

background_survey_1  <- background_survey[1:57,] 
background_survey_1

##            X Please.enter.your.Prolific.ID...Participant.ID..or.full.name X.1
## 1      nvko1                                     5ffc97615991d411f8ceff96  NA
## 2      yrtb9                                     5f22bdb732432502a2615f3f  NA
## 3      ejpg7                                     5fd4125ed9973916d62d9958  NA
## 4      vvpq4                                     5e483052a742a3000f7c15e8  NA
## 5      cfug6                                              Krystle matias   NA
## 6      oucx8                                                        oucx8  NA
## 7      ruvk3                                     5d82d28977546f00166ae702  NA
## 8      myyl9                                                        myyl9  NA
## 9      dhnz3                                                        dhnz3  NA
## 10     bwat2                                     5621cf44ed6e5a000bc7efd1  NA
## 11    ottj9                                                        ottj9   NA
## 12     fjfm3                                     5c920f11be78b00001c82bf0  NA
## 13     buzt0                                     5fd7c279955e160727234b05  NA
## 14     wbje4                                     5a11f4d76924a60001eb7c1b  NA
## 15     wyks1                                     5f5e33a37fa7566accb4e6ac  NA
## 16     tyfi7                                     605cd2aefa163d506547bb2d  NA
## 17     cbky9                                     5a9adf93dbdb470001eee5e1  NA
## 18     rdix8                                     60478deac07fa7000b0f2f2e  NA
## 19     pqvt6                                     5f919274b5383b00096b0295  NA
## 20     iwzp1                                     600ad0fb206b70059718a0c5  NA
## 21     hqlv9                                     5dc71b96d210e45213e92359  NA
## 22     mnwx7                                     6036b303c9b72d01f7d46a5f  NA
## 23     wldp0                                     5d8cef3c28e183001a335ab0  NA
## 24     phww7                                     5bed3079fa1ac70001baa7ad  NA
## 25     bjfc1                                     5e4b78037799c5082e147d5e  NA
## 26     tlit0                                     5f582a0071a90f3972ee9a09  NA
## 27     qeiy3                                     599a9252bbe848000179676e  NA
## 28    onbv9                                                        onbv9   NA
## 29     keta9                                     5d3587a13e11900001093ae8  NA
## 30     kryd6                                     5cf806142780ca001a9d5cee  NA
## 31     qsry8                                     5fa9392c0cc79c03b3f4eb58  NA
## 32     jhoh9                                     5fc63e053cf83c06341ac94a  NA
## 33     fgrl2                                     5fddf3f28ac1824a6fb56e29  NA
## 34     fesx6                                     5f5f5013f947f6000bd8ddcb  NA
## 35     adnm2                                     5ca73fe6bcdcc2001291822d  NA
## 36     vqpg1                                     5af85368f856320001cf6b09  NA
## 37 bi_zefa9                                                        zefa9   NA
## 38  bi_dvyf7                                                        dvyf7  NA
## 39  bi_evur7                                                        evur7  NA
## 40  bi_xcix4                                                        xcix4  NA
## 41  bi_iizs0                                     5d287dfb531d0a0018e9251c  NA
## 42  bi_igsy3                                     5eb471789686d82c24661cb3  NA
## 43  bi_jdyf0                                                        jdyf0  NA
## 44  bi_txmo8                                     5e695632d40a492070942196  NA
## 45  bi_wdfj3                                     5e37fef4d8843b000aeb1b8f  NA
## 46  bi_sgmf1                                     5c60e5371115b4000140fb07  NA
## 47  bi_giah0                                     5c6e26630c45820001b851d3  NA
## 48  bi_ajli0                                                        ajli0  NA
## 49  bi_khem3                                     5ec7d9910125920829519d00  NA
## 50  bi_kxzm6                                                        kxzm6  NA
## 51  bi_qjrz9                                     5fed41e3e14422f5e308c6e5  NA
## 52  bi_dckv1                                     5366a3f1fdf99b66dd29912c  NA
## 53  bi_rftj7                                     5c1d19c810677f0001d9d56c  NA
## 54  bi_ycdf0                                     5d02c0e382e4f00001aa20cd  NA
## 55  bi_nxqq4                                     6005f9ba9a21c96134362ff1  NA
## 56  bi_vpgq2                                     5d4ba0be2fe36d00163e73db  NA
## 57  bi_wetx1                                     5de25e3fce20cd28846a7d5b  NA
##    Bilingual Gender      Residence Age                 Ethnicity
## 1          0      M            USA  22        White or Caucasian
## 2          0      F            USA  30        White or Caucasian
## 3          0      M            USA  32 Asian or Pacific Islander
## 4          0      F            USA  32        White or Caucasian
## 5          0      F            USA  34        White or Caucasian
## 6          0      M            USA  47        Hispanic or Latino
## 7          0      F            USA  35 Asian or Pacific Islander
## 8          0      F            USA  27        White or Caucasian
## 9          0      F            USA  22        Hispanic or Latino
## 10         0      M            USA  34        White or Caucasian
## 11         0      M            USA  38   Multiracial or Biracial
## 12         0      F            USA  35        White or Caucasian
## 13         0      M         Mexico  30        Hispanic or Latino
## 14         0      F United Kingdom  40        White or Caucasian
## 15         0      F United Kingdom  37        White or Caucasian
## 16         0      F   South Africa  35   Multiracial or Biracial
## 17         0      F United Kingdom  49        White or Caucasian
## 18         0      F   South Africa  38 Black or African American
## 19         0      F United Kingdom  29                   turkish
## 20         0      M   South Africa  42 Black or African American
## 21         0      M United Kingdom  43        White or Caucasian
## 22         0      M          Spain  27 Asian or Pacific Islander
## 23         0      F United Kingdom  32        White or Caucasian
## 24         0      M United Kingdom  42        White or Caucasian
## 25         0      F United Kingdom  40 Black or African American
## 26         0      M United Kingdom  47        White or Caucasian
## 27         0      F United Kingdom  56        White or Caucasian
## 28         0      F            USA  50        Hispanic or Latino
## 29         0      F United Kingdom  27        White or Caucasian
## 30         0      F United Kingdom  55        White or Caucasian
## 31         0      F United Kingdom  46        White or Caucasian
## 32         0      F United Kingdom  48        White or Caucasian
## 33         0      M United Kingdom  54        White or Caucasian
## 34         0      F          Spain  33        White or Caucasian
## 35         0      F United Kingdom  36        White or Caucasian
## 36         0      F United Kingdom  44        White or Caucasian
## 37         1      F            USA  43 Asian or Pacific Islander
## 38         1      F            USA  58        White or Caucasian
## 39         1      F            USA  42        Hispanic or Latino
## 40         1      F            USA  50        Hispanic or Latino
## 41         1      F          Spain  25        White or Caucasian
## 42         1      M         Mexico  50        Hispanic or Latino
## 43         1      M            USA  34        White or Caucasian
## 44         1      F United Kingdom  51        White or Caucasian
## 45         1      F    Netherlands  31        White or Caucasian
## 46         1      M         Mexico  46        White or Caucasian
## 47         1      M         Mexico  23        Hispanic or Latino
## 48         1      F            USA  35        Hispanic or Latino
## 49         1      F            USA  26        Hispanic or Latino
## 50         1      F            USA  27        Hispanic or Latino
## 51         1      M            USA  43        White or Caucasian
## 52         1      M United Kingdom  30        White or Caucasian
## 53         1      M         Mexico  44        Hispanic or Latino
## 54         1      F United Kingdom  50        White or Caucasian
## 55         1      F United Kingdom  56        White or Caucasian
## 56         1      M         Mexico  33        Hispanic or Latino
## 57         1      M United Kingdom  49        White or Caucasian
##        Teaching_Level  Experience                   Languages
## 1          9-12 grade   1-3 years                        <NA>
## 2          6-8 grades  7-10 years                        <NA>
## 3          9-12 grade   4-6 years                        <NA>
## 4          9-12 grade  7-10 years                        <NA>
## 5          2nd  grade   4-6 years                        <NA>
## 6          9-12 grade   16+ years                        <NA>
## 7  College/University  7-10 years                        <NA>
## 8        Kindergarten   4-6 years                        <NA>
## 9        Kindergarten   1-3 years                        <NA>
## 10 College/University   4-6 years                        <NA>
## 11       Kindergarten  7-10 years                        <NA>
## 12       Kindergarten   1-3 years                        <NA>
## 13 College/University   4-6 years                        <NA>
## 14          5th grade   16+ years                        <NA>
## 15         9-12 grade 11-15 years                        <NA>
## 16         grade 0-12   4-6 years                        <NA>
## 17 College/University   16+ years                        <NA>
## 18 College/University  7-10 years                        <NA>
## 19 College/University   4-6 years                        <NA>
## 20 College/University 11-15 years                        <NA>
## 21         6-8 grades 11-15 years                        <NA>
## 22 College/University   1-3 years                        <NA>
## 23          3rd grade  7-10 years                        <NA>
## 24 College/University 11-15 years                        <NA>
## 25         9-12 grade   16+ years                        <NA>
## 26 College/University   1-3 years                        <NA>
## 27         9-12 grade   16+ years                        <NA>
## 28       Kindergarten   16+ years                        <NA>
## 29 College/University   1-3 years                        <NA>
## 30 College/University   16+ years                        <NA>
## 31 College/University   16+ years                        <NA>
## 32          1st grade  7-10 years                        <NA>
## 33         9-12 grade   16+ years                        <NA>
## 34         6-8 grades 11-15 years                        <NA>
## 35          4th grade   4-6 years                        <NA>
## 36         9-12 grade   16+ years                        <NA>
## 37         9-12 grade   16+ years                    Mandarin
## 38         6-8 grades  7-10 years                     Spanish
## 39         6-8 grades   4-6 years                     Spanish
## 40         9-12 grade  7-10 years                     Spanish
## 41         6-8 grades   1-3 years Spanish, Basque and English
## 42          4th grade   16+ years                     Spanish
## 43 College/University   4-6 years                     Spanish
## 44          3rd grade   16+ years                      French
## 45         6-8 grades  7-10 years                     English
## 46         6-8 grades   16+ years                     English
## 47 College/University   4-6 years                     English
## 48       Kindergarten  7-10 years                     Spanish
## 49         9-12 grade   1-3 years                     Spanish
## 50         9-12 grade   4-6 years                     Spanish
## 51         9-12 grade   16+ years                     Spanish
## 52         2nd  grade  7-10 years                      French
## 53         9-12 grade 11-15 years                     Spanish
## 54         9-12 grade   16+ years                      French
## 55         9-12 grade   16+ years                      French
## 56          4th grade   1-3 years                     English
## 57          5th grade 11-15 years                      German

library(table1)

## 
## Attaching package: 'table1'

## The following objects are masked from 'package:base':
## 
##     units, units<-

table1(~Bilingual + Age + Ethnicity + Experience | Gender , data = background_survey_1) 

	F (N=36)	M (N=21)	Overall (N=57)
Bilingual
Mean (SD)	0.333 (0.478)	0.429 (0.507)	0.368 (0.487)
Median [Min, Max]	0 [0, 1.00]	0 [0, 1.00]	0 [0, 1.00]
Age
Mean (SD)	39.0 (10.1)	38.6 (9.17)	38.8 (9.67)
Median [Min, Max]	36.5 [22.0, 58.0]	42.0 [22.0, 54.0]	38.0 [22.0, 58.0]
Ethnicity
Asian or Pacific Islander	2 (5.6%)	2 (9.5%)	4 (7.0%)
Black or African American	2 (5.6%)	1 (4.8%)	3 (5.3%)
Hispanic or Latino	7 (19.4%)	6 (28.6%)	13 (22.8%)
Multiracial or Biracial	1 (2.8%)	1 (4.8%)	2 (3.5%)
turkish	1 (2.8%)	0 (0%)	1 (1.8%)
White or Caucasian	23 (63.9%)	11 (52.4%)	34 (59.6%)
Experience
1-3 years	5 (13.9%)	4 (19.0%)	9 (15.8%)
11-15 years	2 (5.6%)	5 (23.8%)	7 (12.3%)
16+ years	12 (33.3%)	5 (23.8%)	17 (29.8%)
4-6 years	7 (19.4%)	5 (23.8%)	12 (21.1%)
7-10 years	10 (27.8%)	2 (9.5%)	12 (21.1%)

table1(~Bilingual + Age+Ethnicity | Gender , data = background_survey_1,topclass = "Rtable1-grid")

	F (N=36)	M (N=21)	Overall (N=57)
Bilingual
Mean (SD)	0.333 (0.478)	0.429 (0.507)	0.368 (0.487)
Median [Min, Max]	0 [0, 1.00]	0 [0, 1.00]	0 [0, 1.00]
Age
Mean (SD)	39.0 (10.1)	38.6 (9.17)	38.8 (9.67)
Median [Min, Max]	36.5 [22.0, 58.0]	42.0 [22.0, 54.0]	38.0 [22.0, 58.0]
Ethnicity
Asian or Pacific Islander	2 (5.6%)	2 (9.5%)	4 (7.0%)
Black or African American	2 (5.6%)	1 (4.8%)	3 (5.3%)
Hispanic or Latino	7 (19.4%)	6 (28.6%)	13 (22.8%)
Multiracial or Biracial	1 (2.8%)	1 (4.8%)	2 (3.5%)
turkish	1 (2.8%)	0 (0%)	1 (1.8%)
White or Caucasian	23 (63.9%)	11 (52.4%)	34 (59.6%)

table1(~Bilingual + Residence + Teaching_Level + Experience | Gender , data = background_survey_1) 

	F (N=36)	M (N=21)	Overall (N=57)
Bilingual
Mean (SD)	0.333 (0.478)	0.429 (0.507)	0.368 (0.487)
Median [Min, Max]	0 [0, 1.00]	0 [0, 1.00]	0 [0, 1.00]
Residence
Netherlands	1 (2.8%)	0 (0%)	1 (1.8%)
South Africa	2 (5.6%)	1 (4.8%)	3 (5.3%)
Spain	2 (5.6%)	1 (4.8%)	3 (5.3%)
United Kingdom	16 (44.4%)	6 (28.6%)	22 (38.6%)
USA	15 (41.7%)	7 (33.3%)	22 (38.6%)
Mexico	0 (0%)	6 (28.6%)	6 (10.5%)
Teaching_Level
1st grade	1 (2.8%)	0 (0%)	1 (1.8%)
2nd grade	1 (2.8%)	1 (4.8%)	2 (3.5%)
3rd grade	2 (5.6%)	0 (0%)	2 (3.5%)
4th grade	1 (2.8%)	2 (9.5%)	3 (5.3%)
5th grade	1 (2.8%)	1 (4.8%)	2 (3.5%)
6-8 grades	6 (16.7%)	2 (9.5%)	8 (14.0%)
9-12 grade	11 (30.6%)	6 (28.6%)	17 (29.8%)
College/University	7 (19.4%)	8 (38.1%)	15 (26.3%)
grade 0-12	1 (2.8%)	0 (0%)	1 (1.8%)
Kindergarten	5 (13.9%)	1 (4.8%)	6 (10.5%)
Experience
1-3 years	5 (13.9%)	4 (19.0%)	9 (15.8%)
11-15 years	2 (5.6%)	5 (23.8%)	7 (12.3%)
16+ years	12 (33.3%)	5 (23.8%)	17 (29.8%)
4-6 years	7 (19.4%)	5 (23.8%)	12 (21.1%)
7-10 years	10 (27.8%)	2 (9.5%)	12 (21.1%)

#plot Language
plot(flanker_correct ~ age + IQ, data = new_language, pch=16)

#linear model
flanker_model_language <- lm(flanker_correct ~ age + IQ, data = new_language)
abline(flanker_model_language)

## Warning in abline(flanker_model_language): only using the first two of 3
## regression coefficients

my_results <- tidy(flanker_model_language)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)   21.2      13.8        1.54  0.141 
## 2 age           -0.155     0.142     -1.09  0.290 
## 3 IQ             0.266     0.131      2.03  0.0573

flanker_model_language

## 
## Call:
## lm(formula = flanker_correct ~ age + IQ, data = new_language)
## 
## Coefficients:
## (Intercept)          age           IQ  
##     21.2068      -0.1552       0.2660

summary(flanker_model_language)

## 
## Call:
## lm(formula = flanker_correct ~ age + IQ, data = new_language)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16.7273  -0.7667   2.4163   3.6353   6.0239 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  21.2068    13.7539   1.542   0.1405  
## age          -0.1552     0.1423  -1.090   0.2899  
## IQ            0.2660     0.1310   2.031   0.0573 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.693 on 18 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2021, Adjusted R-squared:  0.1135 
## F-statistic:  2.28 on 2 and 18 DF,  p-value: 0.131

par(mfrow = c(2,2)) 
plot(flanker_model_language) 

par(mfrow = c(2,2)) 
plot(flanker_model_language, 1)

##
#plot Language
par(mfrow = c(2,2))

plot(stroop_correct ~ age + IQ, data = new_language, pch=16)

#linear model
stroop_model_language <- lm(stroop_correct ~ age + IQ, data = new_language)

par(mfrow = c(2,2))

plot(stroop_model_language)

my_results <- tidy(stroop_model_language)
my_results

## # A tibble: 3 × 5
##   term        estimate std.error statistic  p.value
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)  22.2       4.87        4.55 0.000250
## 2 age          -0.0590    0.0504     -1.17 0.257   
## 3 IQ            0.171     0.0464      3.69 0.00169

stroop_model_language

## 
## Call:
## lm(formula = stroop_correct ~ age + IQ, data = new_language)
## 
## Coefficients:
## (Intercept)          age           IQ  
##    22.15387     -0.05899      0.17098

summary(stroop_model_language)

## 
## Call:
## lm(formula = stroop_correct ~ age + IQ, data = new_language)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4736 -1.1456 -0.2982  1.4977  4.4921 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 22.15387    4.87196   4.547  0.00025 ***
## age         -0.05899    0.05041  -1.170  0.25715    
## IQ           0.17098    0.04639   3.686  0.00169 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.371 on 18 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.4325, Adjusted R-squared:  0.3695 
## F-statistic:  6.86 on 2 and 18 DF,  p-value: 0.006101